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Sid
@sidshekhar
GM Did a deep dive into channels and wrote out key findings: - How much overlap is there between users? - What are the different flavors of chaneloooors? - What's the dispersion of topics (crypto native vs general purpose)? Hope you enjoy π https://paragraph.xyz/@castaway/channels-and-crypto-natives
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Sid
@sidshekhar
s/o top 10 multi-channeloors: 1) @dwr.eth - 215 channels 2) @icetoad.eth - 209 channels 3) @tahiyangan - 185 channels 4) @kmacb.eth - 182 channels 5) @lishousesq4rz - 176 channels 6) @foxgom - 171 channels 7) @gokhan.eth - 168 channels 8) @elesel.eth - 163 channels 9) @artlu - 160 channels 10) @qt - 155 channels
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nick β
@nt
This is fantastic π― How did you get/process the data? I'd love to have a live version of these visualisations
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kbc - q/dau
@kbc
Cool analysis and write up. How did you classify the channels?
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John Camkiran
@johncamkiran
The channel overlap data looks like it holds more insight than can be readily observed from a table. Would be interesting to perform hierarchical clustering to reveal the underlying topology
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ilemi
@ilemi
Nice stuff! 1000 $degen
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kugusha β
@kugusha.eth
That's a great analysis (very well structured, thanks for this!). Curious to know your data sources (if you monitor any Dune or have some vizualization in Notebook) and higher βββ
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Trupty Somaiah π©π¦
@trupty
extremely interesting numbers. Thanks for compiling this 333 $DEGEN
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Danielπ©π£π΅
@dmulchenko
π«‘
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planner
@planner
that is awesome !
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tyler π©
@tmase.eth
Great analysis! Thanks for sharing. 250 $degen
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pradeepbv.eth
@pradeepbv.eth
Thanks for the effort you put in to analyse and visualise this. 420 $DEGEN
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Wub
@wub
Really great work Sid 50 $Degen
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kugusha β
@kugusha.eth
10000 $degen
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